U.S. patent number 10,380,558 [Application Number 15/806,601] was granted by the patent office on 2019-08-13 for intelligent self-service delivery advisor.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is International Business Machines Corporation. Invention is credited to Nikolaos Anerousis, Anup Kalia, Maja Vukovic, Jin Xiao.
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United States Patent |
10,380,558 |
Vukovic , et al. |
August 13, 2019 |
Intelligent self-service delivery advisor
Abstract
The present invention provides a method, system, and computer
program product of an intelligent self-service delivery advisor. In
an embodiment, the present invention includes, in response to
receiving computer system service data, identifying, by a second
computer system, a computer system service category among a
plurality of computer system categories, identifying, by the second
computer system, one or more computer system service tasks, based
on the computer system service data and the computer system service
category, selecting, by the second computer system, a catalog among
a plurality of catalogs, based on the one or more computer system
service tasks and the computer system service data, generating, by
the second computer system, one or more suggestions based on the
catalog and the one or more computer system service tasks; and
displaying, displaying by the second computer system, the one or
more suggestion on a display logically coupled to the computer
system.
Inventors: |
Vukovic; Maja (New York City,
NY), Anerousis; Nikolaos (Los Gatos, CA), Kalia; Anup
(Elmsford, NY), Xiao; Jin (Ossining, NY) |
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
66327426 |
Appl.
No.: |
15/806,601 |
Filed: |
November 8, 2017 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20190139004 A1 |
May 9, 2019 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
10/20 (20130101); G06Q 30/016 (20130101); G06N
20/00 (20190101); G06N 7/005 (20130101); G06N
20/10 (20190101); G06N 3/0445 (20130101); G06N
3/0481 (20130101) |
Current International
Class: |
G06F
9/44 (20180101); G06Q 10/00 (20120101); G06Q
30/00 (20120101); G06N 20/00 (20190101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Mell et al., "The NIST Definition of Cloud Computing,"
Recommendations of the National Institute of Standards and
Technology, US Department of Commerce, Special Publication 800-145,
Sep. 2011, 7 pages. cited by applicant.
|
Primary Examiner: Nahar; Qamrun
Attorney, Agent or Firm: Chaney; Jared C.
Claims
What is claimed is:
1. A method comprising: in response to receiving computer system
service data, identifying, by a second computer system, a computer
system service category among a plurality of computer system
categories; identifying, by the second computer system, one or more
computer system service tasks, based on the computer system service
data and the computer system service category; selecting, by the
second computer system, a catalog among a plurality of catalogs,
based on the one or more computer system service tasks and the
computer system service data; generating, by the second computer
system, one or more suggestions based on the catalog and the one or
more computer system service tasks; displaying, displaying by the
second computer system, the one or more suggestion on a display
logically coupled to the computer system; accessing, by the second
computer system, a skill level of a user with respect to at least
one of the computer system service category among the plurality of
computer system service categories, the one or more computer system
service tasks, and the catalog; and modifying, by the second
computer system, an amount of detail given in the one or more
suggestions, based on the skill level.
2. The method of claim 1, further comprising in response to
receiving a task selection, generating, by the second computer
system, execution content.
3. The method of claim 1, further comprising based on one or more
previous selections by the user, personalizing, by the second
computer system, the one or more suggestions via machine
learning.
4. The method of claim 1, further comprising: in response to the
displaying, identifying, by the second computer system, missing
parameters with respect to the computer system service data;
querying, of a user, one or more of the missing parameters of the
received computer system service data in light of the catalog.
5. The method of claim 4, further comprising in response to
receiving the one or more missing parameters, generating, by the
second computer system, one or more modified suggestions.
6. The method of claim 4, further comprising in response to
receiving the one or more missing parameters, selecting a catalog
among the plurality of catalogs.
7. A system comprising: a memory; and a processor in communication
with the memory, the processor configured to perform a method
comprising; in response to receiving computer system service data,
identify a computer system service category among a plurality of
computer system categories; identify one or more computer system
service tasks, based on the computer system service data and the
computer system service category; select a catalog among a
plurality of catalogs, based on the one or more computer system
service tasks and the computer system service data; generate one or
more suggestions based on the catalog and the one or more computer
system service tasks; display, displaying by the second computer
system, the one or more suggestion on a display logically coupled
to the computer system; access a skill level of a user with respect
to at least one of the computer system service category among the
plurality of computer system service categories, the one or more
computer system service tasks, and the catalog; and modify an
amount of detail given in the one or more suggestions, based on the
skill level.
8. The system of claim 7, further comprising in response to
receiving a task selection, generate execution content.
9. The system of claim 7, further comprising, based on one or more
previous selections by the user, personalizing the one or more
suggestions via machine learning.
10. The system of claim 7, further comprising: in response to the
displaying, identify missing parameters with respect to the
computer system service data; query, of a user, one or more of the
missing parameters of the received computer system service data in
light of the catalog.
11. The system of claim 10, further comprising in response to
receiving the one or more missing parameters, generate one or more
modified suggestions.
12. The system of claim 10, further comprising in response to
receiving the one or more missing parameters, select a catalog
among the plurality of catalogs.
13. A computer program product comprising a computer readable
storage medium having program instructions embodied therewith, the
program instructions executable by a processor to cause the
processor to perform a method comprising: in response to receiving
computer system service data, identifying, by a second computer
system, a computer system service category among a plurality of
computer system categories; identifying, by the second computer
system, one or more computer system service tasks, based on the
computer system service data and the computer system service
category; selecting, by the second computer system, a catalog among
a plurality of catalogs, based on the one or more computer system
service tasks and the computer system service data; generating, by
the second computer system, one or more suggestions based on the
catalog and the one or more computer system service tasks;
displaying, displaying by the second computer system, the one or
more suggestion on a display logically coupled to the computer
system; accessing, by the second computer system, a skill level of
a user with respect to at least one of the computer system service
category among the plurality of computer system service categories,
the one or more computer system service tasks, and the catalog; and
modifying, by the second computer system, an amount of detail given
in the one or more suggestions, based on the skill level.
14. The computer program product of claim 13, further comprising in
response to receiving a task selection, generating, by the second
computer system, execution content.
15. The computer program product of claim 13, further comprising
based on one or more previous selections by the user,
personalizing, by the second computer system, the one or more
suggestions via machine learning.
16. The computer program product of claim 13, further comprising:
in response to the displaying, identifying, by the second computer
system, missing parameters with respect to the computer system
service data; querying, of a user, one or more of the missing
parameters of the received computer system service data in light of
the catalog.
17. The computer program product of claim 16, further comprising in
response to receiving the one or more missing parameters,
generating, by the second computer system, one or more modified
suggestions.
Description
BACKGROUND
The present disclosure relates to integrated circuit chips, and
more specifically, to intelligent self-service delivery
advisor.
SUMMARY
The present invention provides a method, system, and computer
program product of an intelligent self-service delivery advisor. In
an embodiment, the method, system, and computer program product
includes, in response to receiving computer system service data,
identifying, by a second computer system, a computer system service
category among a plurality of computer system categories,
identifying, by the second computer system, one or more computer
system service tasks, based on the computer system service data and
the computer system service category, selecting, by the second
computer system, a catalog among a plurality of catalogs, based on
the one or more computer system service tasks and the computer
system service data, generating, by the second computer system, one
or more suggestions based on the catalog and the one or more
computer system service tasks, and displaying, displaying by the
second computer system, the one or more suggestion on a display
logically coupled to the computer system.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1A depicts a graphical display in accordance with an exemplary
embodiment of the present invention.
FIG. 1B depicts a graphical display in accordance with an exemplary
embodiment of the present invention.
FIG. 1C depicts a graphical display in accordance with an exemplary
embodiment of the present invention.
FIG. 1D depicts a graphical display m in accordance with an
exemplary embodiment of the present invention.
FIG. 1E depicts a graphical display in accordance with an exemplary
embodiment of the present invention.
FIG. 2A depicts a flow diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 2B depicts a flow diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 2C depicts a flow diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 3 depicts a block diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 4 depicts a block diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 5 depicts a block diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 6 depicts a block diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 7 depicts a flow diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 8 depicts a flow diagram in accordance with an exemplary
embodiment of the present invention.
FIG. 9 depicts a computer system in accordance with an exemplary
embodiment of the present invention.
FIG. 10 depicts a cloud computing environment according to various
embodiments of the present invention.
FIG. 11 depicts abstraction model layers according to various
embodiments of the present invention.
DETAILED DESCRIPTION
The present invention provides a method, system, and computer
program product of an intelligent self-service delivery advisor. In
an embodiment, the method, system, and computer program product
includes, in response to receiving computer system service data,
identifying, by a second computer system, a computer system service
category among a plurality of computer system categories,
identifying, by the second computer system, one or more computer
system service tasks, based on the computer system service data and
the computer system service category, selecting, by the second
computer system, a catalog among a plurality of catalogs, based on
the one or more computer system service tasks and the computer
system service data, generating, by the second computer system, one
or more suggestions based on the catalog and the one or more
computer system service tasks, and displaying, displaying by the
second computer system, the one or more suggestion on a display
logically coupled to the computer system.
Executing a change ticket can be a costly and time-consuming
process that can be limited by skill, experience, knowledge,
resources, and time constraints. In an IT service management
process, a change ticket is assigned to an expert who is supposed
to execute it. For example, a change ticket could be a request to
change any parameter (e.g., hardware, software, or power) such as a
change request. The expert has to navigate through an existing
service catalog to serve the request. Each service catalog might
have thousands of options. An expert has to select one such option
and fill-in the parameters to execute the request. Since selecting
an option is limited to skill, experience, knowledge of an expert,
the execution could be inconsistent. Even the request submitted in
the first place could be incomplete and inaccurate. This could
cause a time delay from when the ticket is submitted till it gets
executed. Thus, in an embodiment, the present disclosure describes
an intelligent self-service delivery advisor that enhances the
capability of an expert in interpreting the request and executing
it. In an embodiment, an interactive service delivery advisor
utilizes machine learning to analyze a change request for a
computer system, information on the computer system, and the input
of a user to develop change suggestions, and ultimately execution
of a service ticket, for the computer system.
In an embodiment, based on a request (e.g., an email, an error
report from a user or a computer system, a phone call, or any other
communication from a user or computer system), the user interacts
with a dynamic user interface relevant to the catalog item and that
provides execution context that help a user to accomplish the
required task. In an embodiment, the computer system service data
relates to a change request.
In an embodiment, referring to FIG. 1A, after receiving the
request, a dynamic user interface 100 (displayed on a screen
logically connected to a computer system) lists an initial group of
suggestions 110. In an embodiment, the group of suggestions lists
change categories from a column 120 (for example, hardware) for
each suggestion, change tasks from a column 130 (for example, add
memory) for each suggestion, and a confidence values from a column
140 (for example, 70%) for each suggestion. In an embodiment, the
user interface is called an intelligent advisor.
In an embodiment, the advisor offers suggestions, with certain
confidence to the appropriate catalog item (across multiple
catalogs), that are hierarchical in nature. For example, the change
tasks from column 130 for hardware issues could be: add
memory.fwdarw.remove memory.fwdarw.add CPU. For example, a set of
suggestion for software issues could be, update database
software.fwdarw.database user management.fwdarw.add user. In an
embodiment, each suggestion will be assigned a confidence value to
show the hierarchical nature of the suggestions. For example, a set
of suggestions for a hardware issue with confidence values from
column 140 could be: add memory 70%, remove memory 20%, and add CPU
60%. In an embodiment, a confidence value from column 140 is the
likelihood that a suggestion will resolve the issue.
In an embodiment, the intelligent self-service delivery advisor
includes accessing, by the computer system, accessing, by the
second computer system, a skill level of a user with respect to at
least one of the computer system service category among the
plurality of computer system service categories, the one or more
computer system service tasks, and the catalog, and modifying, by
the second computer system, an amount of detail given in the one or
more suggestions, based on the skill level Referring to FIG. 1B, in
an embodiment, the advisor provides a correctness criterion from
column 150. In an embodiment, the advisor accesses the skill level
of the user and provides appropriate guidance and/or recommendation
on the parameters and execution endpoint for the user to provide
additional information. In an embodiment, based on one or more
previous selections by the user, personalizing, by the second
computer system, the one or more suggestions via machine
learning.
Referring to FIG. 1C, in an embodiment, the dynamic user interface
100 will provide information on why a selection is not available,
e.g., a correctness criterion from column 150. For example, the
advisor provides one or more selections for a user. The advisor
then prompts the user for additional information, such as a
computer system identifier. Based on receiving the additional
information, the advisor processes the additional information with
each selection. When the advisor determines that there is an issue
with the selection, the advisor informs the user of the issue
(e.g., 5 GB memory is not available) by a correctness criterion
from column 150.
Referring to FIG. 1D, in an embodiment, upon receiving information
or data on the user's skill level, the advisor can modify the
information presented on the user interface (i.e., displayed on the
screen) to accommodate the user's skill level. For example, for an
advanced user the display could merely say "5 GB memory is not
available" (as shown in FIG. 1C). Alternatively, for a novice user
could give more details such as "5 GB memory is not available (the
limit is 4 GB. So, kindly, reduce the memory size)." Thus, the
information given to a user is modified to give the user the
information the user needs, based on his skill level, to complete
the service ticket.
In an embodiment, the advisor provides a series of suggestions for
the user to complete and correct a request that would appropriately
map to a catalog service and increase user level of skill and
confidence in the self-service solution.
In an embodiment, the advisor produces a dynamic user interface
(UI) with filled parameters based on the request provided by the
user. Dynamic UI is implemented using different media and support
user accessibility.
In an embodiment, the advisor, based on the history of the user,
computes the skill of the user and refines the suggestions using
the skill of the user such that the level of detail matches the
skill level of the user.
In an embodiment, the intelligent self-service delivery advisor
includes in response to receiving a task selection, generating, by
the second computer system, execution content. Referring to FIG.
1E, in an embodiment, the advisor will summarize the service ticket
and provide a way for the user to submit the service ticket. For
example, the advisor could propose a hardware modification of
adding memory. A box 190 could display that 3 GB of memory will be
added. A box 192 could display the server identifier (e.g.
linuxibm06). A box 194 could display the operating system (OS), in
this case linux. A box 196 could display the action "add" denoting
that the memory will be added. A box 198 could be a "submit" button
that the user can activate to send the service ticket.
In an embodiment, the intelligent self-service delivery advisor is
separated into three tasks. In an embodiments these tasks can be
combined into one operation or series of operations to be performed
sequentially.
Task 1
Referring to FIG. 2A, in an embodiment, the intelligent
self-service delivery advisor has a task 1 200A including an
operation 205 of collecting change requests data, an operation 210
of identifying a category, an operation 215 of identifying tasks,
and an operation 220 of identifying a predicted catalog.
In an embodiment, after receiving a change request from operation
205, operation 210 of identifying a category includes labeling a
set of data (e.g., 1000 rows) with the corresponding labels
(including database, hardware, operating system (OS) management,
etc.), preprocessing the data to remove punctuations and stopwords,
extracting unigram tokens, creating a model using Support Vector
Machine (SVM) classifier, evaluating the model using K-Fold cross
validations, predicting on a new dataset using the model to
evaluate its effectiveness. Stopwords refer to words that are not
useful for predictions. In an embodiment, the labels are used to
identify sections of the catalog with relevant information. In an
embodiment, a unigram token is derived from N-gram where N is
number of co-occurring words in a given window. For example,
consider a change request "increase 2vcpu in server abc01". If N=1,
we extract unigram tokens from the sentence such as "increase",
"2vcpu", "in", "server", and "abc01". If N=2, we extract bigram
tokens such as "increase 2vcpu", "2vcpu in", "in server" and
"server abc01".
In an embodiment, operation 215 of identifying tasks includes
(labeling a set of data (e.g., 1000 rows) with the corresponding
labels, preprocessing the data to remove punctuations and
stopwords, extracting unigram tokens, creating a model using
Support Vector Machine (SVM) classifier, evaluating the model using
K-Fold cross validations, predicting on a new dataset using the
model to evaluate to evaluate its effectiveness) for each category
to identify tasks. For example, for a database, label the dataset
with labels=database update, database backup, database move, and so
on. For example, first a category could be identified
(hardware).
In an embodiment, operation 220 of identifying a predicted catalog
includes labeling words in a sentence, creating a model using
conditional random field (CRF) classifier, perform K-Fold cross
validations, and predicting on a new dataset using the model to
evaluate its effectiveness. For example, for a database, the labels
could be action, database instance, client, and/or server instance.
For example, for hardware, the labels could be action, memory
quantity, and/or server instance. In an embodiment, the catalog is
a data source that has information on a computer system (e.g.,
server, type of server, class of server, computer, type of
computer, class of computer, device, type of device, class of
device, etc.)
Task 2
Referring to FIG. 2B, in an embodiment, intelligent self-service
delivery advisor has a task 2 200B including an operation 230 of
receiving category, tasks, and the catalog, an operation 235 of
providing initial suggestions for the execution of the service
request, an operation 240 of querying the user on additional
parameters by displaying on a screen logically connected to a
computer system, and an operation 245 of receiving user input on
the querying, an operation 250 of a decision step to valid catalog
with "yes" going to FIG. 2C, and "no" going to an operation 265 of
identify catalog and then back to operation 235 of providing
initial suggestions. In an embodiment, operation 240 is a querying
for one or more missing parameters of the requested computer system
service data in light of the catalog. In an embodiment, the method
further includes, in response to receiving the one or more missing
parameters, generating, by the second computer system, one or more
modified suggestions. In an embodiment, the method further
includes, in response to receiving the one or more missing
parameters, selecting a catalog among the plurality of catalogs. In
an embodiment, the method, system, and computer program product
includes, in response to the displaying, identifying, by the second
computer system, missing parameters with respect to the computer
system service data, querying, of a user, one or more of the
missing parameters of the received computer system service data in
light of the catalog.
In an embodiment, the user's input (including cognitive and
contextual state) is analyzed by the advisor to map it to the
appropriate catalog service. Cognitive state is one of more of:
distracted, has a disability/precondition, tired, etc. Context is
one or more of: at the desk, on a mobile device, etc. The advisor
provides a series of suggestions with appropriate confidence values
for the user to make the appropriate selection (e.g. if even to
route the user to self-service solution).
In an embodiment, the intelligent self-service delivery advisor
further includes an operation 255 of receiving the user's skill
level. For example, operation 255 could include receiving a single
qualifier in the form of a number (e.g., level 4, or 40%
proficient) or a qualifier (e.g. novice, intermediate, or
advanced), or receiving information on the user that is used to
determine a skill level of a user. In an embodiment, the user's
skill level is used to determine a level of detail needed for the
suggestions. For example, an advanced or expert user could need
only basic information on a particular suggestion, while a novice
user could need a detailed explanation detailing aspects of each
suggestion or even supplemental references to explain components or
procedures not within the user's knowledge base. For example, if
the advanced user requests DDR 4 RAM with a data rate of 21333
MT/s, the system could provide that the frequency is outside the
valid range of 2133 to 3200 MT/s, whereas a novice user could
simply be told that the requested RAM is invalid and provide the
novice user with a choice of valid selections. In an embodiment,
each user is given a skill level (for example, from 0 to 100), the
skill level is modified over time as the user's skill in the
technological area improves. In an embodiment, for computing skill
of a user, appropriate regression model can be trained with
information on each user to provide a tailored suggestion for each
user. In an embodiment, based on receiving new user information,
the regression model predicts a new skill score for the user. In an
embodiment, the suggestions are refined based on the new skill
score for the user. In an embodiment, the refinement can be made
based on recommendation techniques such collaborative or
content-based filtering techniques. In an embodiment, the
intelligent self-service delivery advisor can learn from the
interaction with the user and user choices to update state and
personalize recommendation for the future (and improve
recommendation to other similar tasks and user roles).
In an embodiment, the intelligent self-service delivery advisor
further includes an operation 260 of receiving context and history
analysis, where the history of the user's selections and input for
previous computer system issues are analyzed to provide input for
the initial suggestions. For example, the history of the user's
previous suggestion choices could be analyzed to narrow the number
of suggestions to only those suggestion the user is likely to
choose. Also, for example, the history of the computer systems that
the user has worked on could be analyzed to determine the likely
computer systems the user is processing the service ticket for.
In an embodiment, operation 240 involves generating queries based
on an analysis of the information needed to generate a subsequent
set of suggestions. In an embodiment, the method repeats one or
more of the operations to before ending the first task. For
example, after completing operation 250 a first time, the
information gleamed from the operations could be used to regenerate
an initial set of suggestions where it is determined that more
information is needed from the user. In an embodiment, upon
receiving the user input, the catalog is validated again.
Task 3
Referring to FIG. 2C, in an embodiment, intelligent self-service
delivery advisor has a task 3 200C including an operation 270 of
using a rule based approach to generate a dynamic user interface,
and operation 280 of retrieving execution context information for
the user (e.g., min./max. entitled CPU, endpoint OS type, etc.) and
render it accordingly in the dynamic user interface. In an
embodiment, based on the parameters and properties in the catalog,
appropriate widgets are rendered in the form of text boxes,
checkboxes, etc. In an embodiment, the intelligent self-service
delivery advisor uses a machine learning based model prepared by
learning which parameter maps what kind of claim requests. For
example, when a user provides a change request, using operation
205, the intelligent self-service delivery advisor preprocess the
data to remove stopwords and lemmatize the words. Lemmatization
provides the base form of a word. Once the intelligent self-service
delivery advisor receives the preprocessed data, in 210 and 215 the
intelligent self-service delivery advisor further extracts relevant
features and then, using a machine learning model trained using
support vector machine (SVM), predicts the category and task
associated with the change request. For example, for a change
request "increase 2vcpu in server abc001", operations 210 and 215
predicts the category as "hardware" and task as "cpu". Once, the
intelligent self-service delivery advisor extracts the task, it
uses operation 220, where it invokes a model trained using
conditional random fields (CRF). The model extracts the parameters
from the change request such as "action: increase", "cpu: 2vcpu",
and "server: abc001". Based on the parameters predicted other
operations such as 230, 235, 240, 245, 250, 255 and 260 are invoked
for recommendation of appropriate parameters.
Referring now to FIG. 3, in an embodiment, an example network
environment 300 includes a plurality of data sources, such as a
source of unstructured textual data 305, error reports 312, user
input 309, emails 307, text messages 315, user information 330,
catalogs 340, and computer system information 345. In an
embodiment, the data sources (e.g., unstructured textual data 305,
error reports 312, user input 309, emails 307, text messages 315,
user information 330, catalogs 340, and computer system information
345) resides in the storage of a single device, or is distributed
across the storage of a plurality of devices. Data collected from
the data sources includes historical data (e.g., data corresponding
to previous repairs). In an embodiment, a single type of data
(e.g., catalogs 340) resides in the storage of a single device, or
resides in the storage of several devices connected either locally
or remotely via a network, such as a network 325. In an embodiment,
the data sources and other devices connected to network 325 are
local to each other, and communicate via any appropriate local
communication medium.
In an embodiment, a data structuring module 320 includes, or is a
part of, a device for converting unstructured, raw data (e.g.,
textual data, images, videos, sound recordings, etc.) into
structured data (e.g., machine-readable data) that a computer
system utilizes.
In an embodiment, user information 330 includes data relevant to
the technical skill of a user. For example, user information 330
could be a self-assessment of technical skill, education, duration
in the user's current job, relevant experience, experience with the
problem in question.
In an embodiment, catalogs 340 includes any data regarding the
computer or server systems. For example, catalogs 340 includes
repair/servicing information for a particular computer system
(e.g., a server), a class of computer system (e.g. a class of
servers), general repair/servicing information, and/or general
computer system information.
In an embodiment, computer system information 345 includes data
collected from or about the computer system involved in the system
ticket. For example, an email reporting a computer system issue
could include information on the computer system. For example, in a
computer system generated report, the computer system could include
identification information.
In an embodiment, the various data sources, the data structuring
module 320 and a statistical analyzer 350 are connected via network
325. The network 325 can be implemented using any number of any
suitable communications media. For example, the network 325 could
be a wide area network (WAN), a local area network (LAN), an
internet, or an intranet. For example, the data structuring module
320 and statistical analyzer 350 and one or more data sources could
communicate using a local area network (LAN), one or more hardwire
connections, a wireless link or router, or an intranet. In an
embodiment, the data structuring module 320, statistical analyzer
350, and/or one or more data sources are communicatively coupled
using a combination of one or more networks and/or one or more
local connections. For example, the data structuring module 320 is
hardwired to the statistical analyzer 350 (e.g., connected with an
Ethernet cable) while the data sources could communicate with the
data structuring module 320 and statistical analyzer 350 using the
network 325 (e.g., over the Internet).
In an embodiment, the network 325 can be implemented within a cloud
computing environment, or using one or more cloud computing
services. Consistent with various embodiments, a cloud computing
environment includes a network-based, distributed data processing
system that provides one or more cloud computing services. Further,
a cloud computing environment includes many computers (e.g.,
hundreds or thousands of computers or more) located within one or
more data centers and configured to share resources over the
network 325.
In an embodiment, data structuring module 320 and/or statistical
analyzer 350 employs "crawlers" or "scrapers" to access the various
data sources to mine relevant data at particular intervals, or in
real-time. Crawlers/scrapers are configured to "patrol" in search
of relevant data (e.g., unstructured textual data 305, error
reports 312, user input 309, emails 307, text messages 315, user
information 330, catalogs 340, computer system information 345,
etc.) in the data sources, such error reports 312, user input 309,
emails 307, text messages 315, user information 330, catalogs 340,
and computer system information 345. For example, a crawler is
configured to identify and retrieve information on the servicing
issue, to identify and retrieve information on the computer system,
to identify and retrieve change request records for a particular
demographic or a particular individual, etc. Crawlers are
configured to "crawl" through a database or data source at a given
interval, and/or to retrieve documents that have been updated or
modified subsequent to a previous retrieval. A document fitting the
crawler's parameters is retrieved, and if needed, analyzed and
converted from an unstructured state into a structured state via
data structuring module 320.
In an embodiment, structured data is said to contain sets of
features (e.g., events preceding, attributes, characteristics,
etc.) of computer issues. The data from each data source is said to
contain a single feature set. For example, the data from the source
containing error reports is a first feature set, the data from the
source containing computer system information 345 is a second
feature set, and so on. Once the feature set from each available
data source is collected, it is combined to create a complete
feature set.
In an embodiment, a complete feature set (e.g., a set of all
features related to a computer system issue, computer system, or
user skill level) is utilized by statistical analyzer 350, using
the methods described herein (e.g., kMeans clustering), to
determine correlations between features (e.g., characteristics,
user skill level, technical requirements, etc.) of a particular
computer system issue (e.g., requirement, malfunctioning
components, etc.) and possible solutions. For example, statistical
analyzer 350 could identify that memory is low in a particular
computer system. Possible solutions could include replacing memory,
installing new more efficient software, etc. In an embodiment,
k-means clustering aims to partition n observations into k clusters
in which each observation belongs to the cluster with the nearest
mean, serving as a prototype of the cluster.
In an embodiment, statistical analyzer 350 generates suggestions
355. In an embodiment, suggestions 355 are generated by considering
all data relevant to the change request (e.g., a plurality of
complete feature sets) that correlate to fulfilling the change
request. Particular features within the suggestions 355 are
weighted. For example, replacing the memory with a higher memory
component is more likely to resolve the issue than upgrading
software. In one example, the confidence value for adding new
memory could have a confidence value (likelihood of resolving the
issue) of 90% and upgrading the software could have a confidence
value of 10%.
Referring to FIG. 4, in an embodiment, illustrated is a block
diagram of an example natural language processing system configured
to analyze error data, user skill information, or any other report
with unstructured textual data, in accordance with embodiments of
the present disclosure. In an embodiment, a remote device (such as
a device containing one or more of the data sources described in
FIG. 3) submits electronic documents (such as textual error
reports, or other unstructured textual reports) to be analyzed to a
natural language processing system 412 which is a standalone
device, or part of a larger computer system. In an embodiment,
natural language processing system 412 includes a client
application 408, which itself involves one or more entities
operable to generate or modify information in the unstructured
textual report(s) that is then dispatched to natural language
processing system 412 via a network 415, which in some embodiments
is consistent with network 325.
In an embodiment, the natural language processing system 412
responds to electronic document submissions sent by client
application 408. In an embodiment, specifically, natural language
processing system 412 analyzes a received unstructured textual
report (e.g., unstructured textual data 305, error reports 312,
user input 309, emails 307, text messages 315, user information
330, catalogs 340, computer system information 345, etc.) to
identify a feature or feature set (e.g., one or more
characteristics of the error report, such as the specific server,
problem or issue, desired performance parameter, system limitation,
etc.), and one or more suggestion (e.g., how to resolve the service
issue).
Likewise, in an embodiment, natural language processing system 412
analyzes a received unstructured textual report relating to the
user skill level. If a user skill level is not provided to client
application 408, a skill of a user could be determined using
natural language processing system 412 to analyze information
relating to the user's technical skill. For example, natural
language processing system 412 could analyze a received
unstructured textual report including the user's education,
experience, etc. to determine a relative skill level.
In an embodiment, natural language processing system 412 includes a
natural language processor 414, data sources 424, a search
application 428, and a report analysis module 430. Natural language
processor 414 is a computer module that analyzes the received
unstructured textual reports and other electronic documents. In an
embodiment, natural language processor 414 performs various methods
and techniques for analyzing electronic documents (e.g., syntactic
analysis, semantic analysis, etc.). Natural language processor 414
is configured to recognize and analyze any number of natural
languages. In an embodiment, natural language processor 414 parses
passages of the documents. Further, natural language processor 414
includes various modules to perform analyses of electronic
documents. These modules include, but are not limited to, a
tokenizer 416, a part-of-speech (POS) tagger 418, a semantic
relationship identifier 420, and a syntactic relationship
identifier 422.
In an embodiment, tokenizer 416 is a computer module that performs
lexical analysis. In an embodiment, tokenizer 416 converts a
sequence of characters into a sequence of tokens. A token is a
string of characters included in an electronic document and
categorized as a meaningful symbol. Further, in an embodiment,
tokenizer 416 identifies word boundaries in an electronic document
and breaks any text passages within the document into their
component text elements, such as words, multiword tokens, numbers,
and punctuation marks. In an embodiment, tokenizer 416 receives a
string of characters, identify the lexemes in the string, and
categorizes them into tokens.
In an embodiment, POS tagger 418 is a computer module that marks up
a word in passages to correspond to a particular part of speech. In
an embodiment, POS tagger 418 reads a passage or other text in
natural language and assigns a part of speech to each word or other
token. In an embodiment, POS tagger 418 determines the part of
speech to which a word (or other text element) corresponds, based
on the definition of the word and the context of the word. The
context of a word is based on its relationship with adjacent and
related words in a phrase, sentence, or paragraph. In an
embodiment, the context of a word is dependent on one or more
previously analyzed electronic documents. In an embodiment, the
output of natural language processing system 412 populates a text
index, a triple store, or a relational database to enhance the
contextual interpretation of a word or term. Examples of parts of
speech that is assigned to words include, but are not limited to,
nouns, verbs, adjectives, adverbs, and the like. Examples of other
part of speech categories that POS tagger 418 could assign include,
but are not limited to, comparative or superlative adverbs,
wh-adverbs, conjunctions, determiners, negative particles,
possessive markers, prepositions, wh-pronouns, and the like. In an
embodiment, POS tagger 418 tags or otherwise annotates tokens of a
passage with part of speech categories. In an embodiment, POS
tagger 418 tags tokens or words of a passage to be parsed by
natural language processing system 412.
In an embodiment, semantic relationship identifier 420 is a
computer module that is configured to identify semantic
relationships of recognized text elements (e.g., words, phrases) in
documents. In an embodiment, semantic relationship identifier 420
determines functional dependencies between entities and other
semantic relationships.
Consistent with various embodiments, the syntactic relationship
identifier 422 is a computer module that is configured to identify
syntactic relationships in a passage composed of tokens. In an
embodiment, the syntactic relationship identifier 422 determines
the grammatical structure of sentences. For example, which groups
of words are associated as phrases and which word is the subject or
object of a verb. In an embodiment, the syntactic relationship
identifier 422 conforms to formal grammar.
In an embodiment, natural language processor 414 is a computer
module that parses a document and generates corresponding data
structures for one or more portions of the document. For example,
in response to receiving an unstructured textual report at natural
language processing system 412, natural language processor 414
could output parsed text elements from the report as data
structures. In an embodiment, a parsed text element is represented
in the form of a parse tree or other graph structure. To generate
the parsed text element, natural language processor 414 triggers
computer modules 416-422.
In an embodiment, the output of natural language processor 414 is
used by search application 428 to perform a search of a set of
(i.e., one or more) corpora to retrieve one or more features, or
sets of features, and one or more associated criteria to send to an
image processing system and to a comparator. A comparator is, for
example, a statistical analyzer, such as statistical analyzer 350
of FIG. 3. In an embodiment, as used herein, a corpus refers to one
or more data sources, such as data sources 424 of FIG. 4, or the
various data sources described in FIG. 3. In an embodiment, data
sources 424 includes data warehouses, information corpora, data
models, and document repositories. In an embodiment, data sources
424 includes an information corpus 426. In an embodiment,
information corpus 426 enables data storage and retrieval. In an
embodiment, information corpus 426 is a storage mechanism that
houses a standardized, consistent, clean, and integrated list of
features. In an embodiment, information corpus 426 also stores, for
each feature, a list of associated suggestions. For example,
information corpus 426 includes the types of computer system
components involved (e.g., CPUs, memory, hard drives, graphics
cards, software, etc.) and for each occurrence of computer system
components, associated suggestions (e.g., change CPU or change
memory) is listed. The data is sourced from various operational
systems. Data stored in information corpus 426 is structured in a
way to specifically address reporting and analytic requirements. In
an embodiment, information corpus 426 is a data repository, a
relational database, triple store, or text index.
In an embodiment, report analysis module 430 is a computer module
that identifies a feature and a suggestion by analyzing one or more
unstructured textual reports (e.g., error report or system
modification request). In an embodiment, report analysis module 430
includes a feature identifier 432 and a suggestion identifier 434.
When an unstructured textual report is received by natural language
processing system 412, report analysis module 430 is configured to
analyze the report using natural language processing to identify
one or more features. In an embodiment, report analysis module 430
first parses the report using natural language processor 414 and
related subcomponents 416-422. After parsing the report, feature
identifier 432 identifies one or more features present in the
report. This is done by, e.g., searching a dictionary (e.g.,
information corpus 426) using search application 428. In an
embodiment, once a feature is identified, feature identifier 432 is
configured to transmit the feature to an image processing system
and/or to a statistical analyzer (such as shown in FIG. 3).
In an embodiment, suggestion identifier 434 identifies one or more
suggestions (e.g., service suggestions) in one or more unstructured
textual reports. This is done by using search application 428 to
comb through the various data sources (e.g., information corpus 426
or the data sources discussed in FIG. 3) for information and/or
reports regarding various service categories (e.g., hardware
repair, hardware replacement, software updating, software
replacement, etc.) associated with a particular computer system
issue. In an embodiment, the list of possible suggestions is
predetermined and information related to the list of suggestions
(e.g., cost, complexity, availability, likelihood of resolving the
issue, etc.) is populated as suggestion information is retrieved.
In an embodiment, suggestion identifier 434 searches, using natural
language processing, reports from the various data sources for
terms in the list of suggestions. After identifying a list of
suggestions, suggestion identifier 434 is configured to transmit
the list of suggestions to a statistical analyzer (shown in FIGS.
3, 5, and 6).
In an embodiment, referring to FIG. 5, shown is a block diagram of
an example high level architecture of a system 500 for structuring
unstructured textual and visual data, in accordance with
embodiments of the present disclosure. In an embodiment, a data
structuring module 501 and a statistical analyzer 522 includes the
same characteristics as the data structuring module 320 and
statistical analyzer 350 of FIG. 3, respectively. In an embodiment,
a remote device 502 is substantially similar to one or more of the
various data sources described in FIG. 3 and submits data to a
document receiving module 504. The data includes one or more
reports and one or more images or videos, such as computer
diagrams. Document receiving module 504 is configured to receive
the data and to send image(s) and video(s) to an image processing
system 506 and report(s) to a natural language processing system
514. In an embodiment, some reports (e.g., error reports, skill
level reports, etc.) contain both images and text; document
receiving module 504 is configured to parse the data to separate
the images and text prior to sending the data to image processing
system 506 or to a natural language processing system 514.
In an embodiment, a natural language processing system 514 includes
the same modules and components as natural language processing
system 412 (shown in FIG. 4). Natural language processing system
514 includes, e.g., a natural language processor 516, a search
application 518, and a report analysis module 520. Natural language
processing system 514 is configured to analyze the textual
reports/data to identify one or more features and one or more
suggestions relating to the feature(s). In an embodiment, after
identifying a feature and a suggestion, natural language processing
system 514 transmits the feature and suggestion to image processing
system 506. In an embodiment, natural language processing system
514 also transmits both the feature and the suggestion to
statistical analyzer 522. Report analysis module 520 is
substantially similar to report analysis module 430 of FIG. 4.
In an embodiment, image processing system 506 includes, e.g., a
skill level module 508, an image analysis module 510, and a feature
& suggestion receiving module 512. Feature & suggestion
receiving module 512 is configured to receive, from natural
language processing system 514, identified features &
suggestions determined by analyzing one or more unstructured
textual reports that are related to images/videos received from
document receiving module 504. In an embodiment, based on digital
file formats (e.g., image file formats and video file formats),
image processing system 506 determines with which image processing
module (e.g., skill level module 508 or image analysis module 510)
the system should analyze the image/video received from document
receiving module 504.
In an embodiment, skill level module 508 is configured to
recognize, parse, and output structured data representations of
hand-drawn and computer-generated computer diagrams, such as, for
example, computer performance graphs, screen shots, or diagrams in
technician notes. In an embodiment, skill level module 508
interprets, a relative skill level of a user and the degree of
information needed for each suggestion based on the user skill
level information and the complexity of the suggestion based on
relevant information (such as a catalog or a database containing
task complexity).
In an embodiment, image analysis module 510 is configured to
recognize computer diagrams, still images, or screen shots and
output structured data representations (e.g., machine-readable
data) of computer system-related data therein. For example, image
analysis module 510 is configured to identify, from a still image,
a video, or a single frame of a video feed, features and/or
suggestions represented in the image or video (e.g., computer
parts, computer readouts, computer performance graphs on a
screenshot, etc.).
In an embodiment, feature & suggestion receiving module 512
receives features and suggestions identified by report analysis
module 520. Features and suggestions identified by report analysis
module 520 are related to computer diagrams, images, screenshots or
video processed by image processing system 506 (for example, a
screenshot or video of a performance graph in a task manager
window). In an embodiment, document receiving module 504 parses the
screenshot or video and sends the unstructured text portion to
natural language processing system 514 and the graphs to image
processing system 506. In an embodiment, portions of the data that
do not need image processing are sent to statistical analyzer 522.
Feature & suggestion receiving module 512 is configured to
receive the features and suggestions identified from the textual
portion of the image and combine them with the features and
suggestions identified from the diagrams of that image to ensure
that a robust set of features and suggestions for the particular
computer service issue are identified and grouped together.
After image processing system 506 has analyzed any received
images/diagrams/videos and natural language processing system 514
has analyzed any received unstructured textual reports for a given
computer system issue, the complete feature set (e.g., all the
feature sets related to a particular computer system issue) and
suggestions are sent to statistical analyzer 522.
In an embodiment, as discussed herein, statistical analyzer 522
determines (e.g., using kMeans or other statistical techniques)
which features correlate to which suggestions. For example, if it
is determined that free memory (e.g., a feature identified from a
screenshot containing a memory usage graph) on a system is below a
certain threshold (for example below a certain level suggested by a
software manufacture's suggestion). As a result, statistical
analyzer 522 could suggest that either memory be freed by upgrading
or uninstalling software.
In an embodiment, it is determined that features or characteristics
of a computer system require certain upgrades. For example, data
structuring module 501 could provide the relative computer system
issue information to statistical analyzer 522, allowing statistical
analyzer 522 to determine likely solutions and their confidence
values. Further, for example, data structuring module 501 could
provide the relative user skill information to statistical analyzer
522, allowing statistical analyzer 522 to determine the amount of
information the detail level to be given for each suggestion by
determining the user's skill level for each suggestion.
After statistical analyzer 522 has digested a sufficient number of
features and suggestions received from data structuring module 501
(e.g., the number of features and suggestions required for a robust
and reliable computer change model for change suggestions,
confidence values, and determining the detail level needed for each
suggestion due to the user's skill), a model feature set is output
to a recursive neural network 524. A model feature set includes
features from a wide variety of computer system issues and change
suggestions for the computer system issues. A model feature set is
a static set of data, or it is dynamically updated "on-the-fly" as
statistical analyzer 522 continuously receives additional features
and suggestions from data structuring module 501.
In an embodiment, recursive neural network 524 is a multi-layer
perceptron, a system of sigmoid neurons, a directed acyclic graph
comprising a plurality of corelets, or any other structure/system
capable of neural networking.
In an embodiment, recursive neural network 524 is used to conduct
simulations of computer service issues wherein certain parameters
of the simulation (e.g. certain features) are defined and/or
manipulated by one or more users. Such simulations are used to
determine that novel features (e.g., features not encountered or
identified from any reports from the various data sources) or
uncommon features are at issue than conventional or common change
solutions.
In an embodiment, recursive neural network 524 utilizes the model
feature set to analyze real-time input received from the sensors of
the computer system and determine which suggestion is most likely
to provide a solution to the issue. In an embodiment, recursive
neural network 524 determines that no solution would sufficiently
resolve the issue. For example, recursive neural network 524 could
determine that the current system could not handle the requisite
memory and determine that the system is not capable of performing
with the requested performance metrics.
Referring now to FIG. 6, illustrated is a block diagram of an
example computing environment 600 for creating computer change
models and employing them to assist a user in selecting a solution,
in accordance with embodiments of the present disclosure.
Consistent with various embodiments, the host device 621, the data
repository 602, and a remote device 612 include, or are, computer
systems. The host device 621, the data repository 602, and remote
device 612 each includes one or more processors 626, 606, and 616
and one or more memories 628, 608, and 618, respectively. The host
device 621, the data repository 602, and remote device 612 are
configured to communicate with each other through an internal or
external network interface 624, 604, and 614, respectively. In an
embodiment, the network interfaces 624, 604, and 614 are, e.g.,
modems or network interface cards. In an embodiment, the host
device 621, the data repository 602, and remote device 612 is
equipped with a display or monitor (not pictured). Additionally, in
an embodiment, the host device 621, the data repository 602, and
remote device 612 include optional input devices (e.g., a keyboard,
mouse, scanner, or other input device), and/or any commercially
available or custom software (e.g., browser software,
communications software, server software, speech recognition
software, natural language processing software, search engine
and/or web crawling software, filter modules for filtering content
based upon predefined parameters, etc.). In an embodiment, the host
device 621, the data repository 602, and remote device 612 include
or are servers, desktops, laptops, or hand-held devices.
In an embodiment, host device 621, the data repository 602, and
remote device 612 is distant from each other and communicate over a
network 650. In an embodiment, the host device 621 is a central hub
from which data repository 602 and remote device 612 can establish
a communication connection, such as in a client-server networking
model. Alternatively, the host device 621, the data repository 602,
and remote device 612 are configured in any other suitable
networking relationship (e.g., in a peer-to-peer configuration or
using any other network topology).
In an embodiment, data repository 602 is substantially similar to
any or all of the various data sources discussed in FIG. 3, data
sources 424 of FIG. 4, or remote device 502 of FIG. 5. In an
embodiment, data repository 602 submits data, using data submission
module 610, via network 650 to host device 621. In an embodiment,
host device 621 then generates a computer change model to be used
in determining the change requests that remote device 612 will
execute.
In an embodiment, remote device 612 enables users to submit (or
submits automatically with or without user input) electronic data
(e.g., real-time computer system status) to the host device 621 in
order to identify real-time features to utilize in a computer
change model for determining computer changes for remote device
612. For example, remote device 612 includes real-time data
submission module 620 and a user interface (UI). The UI is any type
of interface (e.g., command line prompts, menu screens, graphical
user interfaces). The UI allows a user to interact with the host
device 621 to submit, using the real-time data submission module
620, real-time features to the host device 621.
In an embodiment, the host device 621 includes a data structuring
module 622. Data structuring module 622 is substantially similar to
data structuring module 320 of FIG. 3, or data structuring module
501 of FIG. 5.
In an embodiment, the data structuring module 622 includes a
natural language processing system 632, which is substantially
similar to natural language processing system 412 of FIG. 4 or
natural language processing system 514 of FIG. 5. The natural
language processing system 632 includes a natural language
processor 634, a search application 636, and a report analysis
module 638. The natural language processor 634 can include numerous
subcomponents, such as a tokenizer, a part-of-speech (POS) tagger,
a semantic relationship identifier, and a syntactic relationship
identifier.
The search application 636 is implemented using a conventional or
other search engine, and is distributed across multiple computer
systems. The search application 636 is configured to search one or
more databases, as described herein, or other computer systems for
content that is related to an electronic document (such as an error
report) submitted by, or retrieved from, a data repository 602. For
example, the search application 636 is configured to search
dictionaries, catalogs, and/or archived error reports to help
identify one or more features, and suggestions associated with the
features, relating to the change request. The report analysis
module 638 is configured to analyze an error reports or service
requests to identify service and suggestions (e.g., steps to be
taken to resolve the issue). In an embodiment, the report analysis
module 638 includes one or more modules or units, and utilizes the
search application 636, to perform its functions (e.g., to identify
a feature and a suggestion), as discussed in more detail in
reference to FIGS. 3-5.
In an embodiment, the data structuring module 622 includes an image
processing system 642. Image processing system 642 is substantially
similar to image processing system 506 of FIG. 5. In an embodiment,
image processing system considers 642 features and suggestions
identified by the natural language processing system 632 (e.g.,
features and suggestions received by feature & suggestion
receiving module 648) when identifying features and suggestions
from an image, video, or diagram received or retrieved from data
repository 602. In an embodiment, image processing system 642
utilizes one or more models, modules, or units to perform its
functions (e.g., to analyze an image/video/diagram and identify
feature sets and suggestions). For example, image processing system
642 could include one or more image processing modules that are
configured to identify specific features and suggestions in an
error report, service request, screen shot, recording of a display,
etc. The image processing modules include, by way of example, a
graph diagram module 644 to analyze computer performance diagrams
and graphs to identify features and suggestions. As another
example, image processing system 642 includes an image analysis
module 646 to identify features and suggestion from screen shots,
videos, computer diagrams, and real-time performance graphics. In
an embodiment, the image processing modules are implemented as
software modules. In an embodiment, graph diagram module 644 and
image analysis module 646 are combined into a single software
module or divided among the several components of the host device
621 or the data structuring module 622.
In an embodiment, image processing system 642 includes a feature
& suggestion receiving module 648. The feature & suggestion
receiving module 648 is substantially similar to feature &
suggestions receiving module 512 of FIG. 5.
In an embodiment, the host device 621 includes a statistical
analyzer 630. The statistical analyzer 630 is configured to receive
features and suggestions from the natural language processing
system 632 and an image analysis from image processing system 642
(e.g., the statistical analyzer 630 is substantially similar to the
statistical analyzer 350 of FIGS. 3 and 522 of FIG. 5).
In an embodiment, the data structuring module 622 has an optical
character recognition (OCR) module (not pictured). In an
embodiment, the OCR module is configured to receive an analog
format of an unstructured textual report sent from a data
repository 602 and perform optical character recognition (or a
related process) on the report to convert it into machine-encoded
text so that the natural language processing system 632 performs
natural language processing on the report. For example, the data
repository 602 could transmit an image of a scanned service request
to the host device. The OCR module could convert the image into
machine-encoded text, and then the converted report is sent to the
natural language processing system 632 for analysis. In an
embodiment, the OCR module is a subcomponent of the natural
language processing system 632. In other embodiments, the OCR
module is a standalone module within the host device 621 or data
structuring module 622. In still other embodiments, the OCR module
is located within the data repository 602 and performs OCR on the
unstructured, analog textual reports before they are sent to the
host device 621 or data structuring module 622.
In an embodiment, host device 621 further includes storage 631 for
storing features, suggestions, and computer system change request
models. Computer system change request models are loaded into
active memory (e.g., memory 628 or memory 618) to process real-time
input (e.g., data received from real-time data submission module
620) to determine a set of change suggestions that a remote device
612 should execute in light of real-time features (e.g., current
memory usage).
While FIG. 6 illustrates a computing environment 600 with a single
host device 621, a single data repository 602, and a single remote
device 612, suitable computing environments for implementing
embodiments of this disclosure includes any number of host devices,
data repositories, and remote devices. In an embodiment, the
various models, modules, systems, and components discussed in
relation to FIG. 6 exist, if at all, across a plurality of host
devices, data repositories, and remote devices. For example, some
embodiments include two host devices and multiple data
repositories. The two host devices are communicatively coupled
using any suitable communications connection (e.g., using a WAN, a
LAN, a wired connection, an intranet, or the Internet). The first
host device includes a natural language processing system
configured to receive and analyze unstructured textual reports, and
the second host device includes an image processing system
configured to receive and analyze diagrams, images, or
screenshots.
In an embodiment, it is noted that FIG. 6 is intended to depict the
representative major components of an exemplary computing
environment 600. In an embodiment, however, individual components
have greater or lesser complexity than as represented in FIG. 6,
components other than or in addition to those shown in FIG. 6 are
present, and the number, type, and configuration of such components
may vary.
In an embodiment, referring now to FIG. 7, shown is a method 700
for generating a model feature set, in accordance with embodiments
of the present disclosure. At 708, data is received. Data includes,
for example, data from any of the sources discussed in relation to
FIGS. 3-4. Data could be received in response to a query (e.g.,
"pulled") of a data source, or data could be received automatically
or at specific intervals from a data source (e.g., "pushed").
In an embodiment, at 710, it is determined whether the received
data is structured. Structured data includes machine-readable data
or any data that does not require further processing to be utilized
in a statistical analysis or the generation of a computer change
model.
In an embodiment, if it is determined, at 710, that received data
is not structured, unstructured data is converted into structured
data at 715. Techniques for converting unstructured data into
structured data are discussed in detail in the descriptions of
FIGS. 3-6 and includes, for example, natural language processing
techniques, image processing techniques, optical character
recognition, etc. In an embodiment, if it is determined, at 710,
that received data is structured, the method proceeds to 720.
In an embodiment, at 720, it is determined if sufficient data has
been received. For example, in order to build robust and effective
models, a certain volume of data (e.g., a certain number of data
entries for a number of data points in a statistical analysis) or a
particular sample size is required. A threshold for determining
whether a sufficient amount of data has been received is employed,
and the threshold is based on user input or standards for
statistical analyses that are well-known in the art.
In an embodiment, if, at 720, it is determined that sufficient data
has been received, features and outcomes are identified at 725.
Features include the conditions that lead or contribute to the
occurrence of a computer service issue. Outcomes include the
results of previous computer service issues and their solutions. In
an embodiment, if, at 720, it is determined that sufficient data
has not been received, the user is queried for more information and
the additional data is received at 708.
In an embodiment, at 730, statistical analyses are performed to
characterize the features and outcomes. Techniques for performing
statistical analyses (e.g., clustering techniques), are described
in greater detail herein.
In an embodiment, at 735, correlations between the features and the
outcomes are identified, as described herein.
In an embodiment, at 740, a model feature set is generated, based
on the correlations identified. For example, as described herein,
features (e.g., bad screen resolution, lack of memory, slow
processing, etc.) could correlate to an increased (or decreased)
risk of the occurrence of a computer issue, and could further
correlate to an increased (or decreased) level of severity
regarding service suggestions (e.g., install new software, replace
hard drive, add more memory, etc.). A model feature set includes
rules, algorithms, neural network configurations/parameters, etc.
representing these correlations. As such, a model feature set is
utilized to perform, for example, a computer simulation of a
computer service issue according to a list of selected features or
to determine which computer service suggestion should execute to
maximize the likelihood of solving the issue, given a set of
unalterable real-time features (e.g., computer system
conditions).
In an embodiment, a model feature set is dynamic. In other words,
the model feature set may update "on-the-fly" as more data is
received and processed to produce more accurate correlation
representations from the increased sample size.
In an embodiment, method 800 includes, an operation 810 of in
response to receiving computer system service data, identifying, by
a second computer system, a computer system service category among
a plurality of computer system categories, an operation 820 of
identifying, by the second computer system, one or more computer
system service tasks, based on the computer system service data and
the computer system service category, an operation 830 of
selecting, by the second computer system, a catalog among a
plurality of catalogs, based on the one or more computer system
service tasks and the computer system service data, an operation
840 of generating, by the second computer system, one or more
suggestions based on the catalog and the one or more computer
system service tasks, and an operation 850 of displaying,
displaying by the second computer system, the one or more
suggestion on a display logically coupled to the computer
system.
Computer System
In an exemplary embodiment, the computer system is a computer
system 900 as shown in FIG. 9. Computer system 900 is only one
example of a computer system and is not intended to suggest any
limitation as to the scope of use or functionality of embodiments
of the present invention. Regardless, computer system 900 is
capable of being implemented to perform and/or performing any of
the functionality/operations of the present invention.
Computer system 900 includes a computer system/server 912, which is
operational with numerous other general purpose or special purpose
computing system environments or configurations. Examples of
well-known computing systems, environments, and/or configurations
that may be suitable for use with computer system/server 912
include, but are not limited to, personal computer systems, server
computer systems, thin clients, thick clients, hand-held or laptop
devices, multiprocessor systems, microprocessor-based systems, set
top boxes, programmable consumer electronics, network PCs,
minicomputer systems, mainframe computer systems, and distributed
cloud computing environments that include any of the above systems
or devices.
Computer system/server 912 may be described in the general context
of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
and/or data structures that perform particular tasks or implement
particular abstract data types. Computer system/server 912 may be
practiced in distributed cloud computing environments where tasks
are performed by remote processing devices that are linked through
a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 9, computer system/server 912 in computer system
900 is shown in the form of a general-purpose computing device. The
components of computer system/server 912 may include, but are not
limited to, one or more processors or processing units 916, a
system memory 928, and a bus 918 that couples various system
components including system memory 928 to processor 916.
Bus 918 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
Computer system/server 912 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 912, and includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 928 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 930
and/or cache memory 932. Computer system/server 912 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 934 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 918 by one or more data
media interfaces. As will be further depicted and described below,
memory 928 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions/operations of embodiments of the
invention.
Program/utility 940, having a set (at least one) of program modules
942, may be stored in memory 928 by way of example, and not
limitation. Exemplary program modules 942 may include an operating
system, one or more application programs, other program modules,
and program data. Each of the operating system, one or more
application programs, other program modules, and program data or
some combination thereof, may include an implementation of a
networking environment. Program modules 942 generally carry out the
functions and/or methodologies of embodiments of the present
invention.
Computer system/server 912 may also communicate with one or more
external devices 914 such as a keyboard, a pointing device, a
display 924, one or more devices that enable a user to interact
with computer system/server 912, and/or any devices (e.g., network
card, modem, etc.) that enable computer system/server 912 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 922.
Still yet, computer system/server 912 can communicate with one or
more networks such as a local area network (LAN), a general wide
area network (WAN), and/or a public network (e.g., the Internet)
via network adapter 920. As depicted, network adapter 920
communicates with the other components of computer system/server
912 via bus 918. It should be understood that although not shown,
other hardware and/or software components could be used in
conjunction with computer system/server 912. Examples, include, but
are not limited to: microcode, device drivers, redundant processing
units, external disk drive arrays, RAID systems, tape drives, and
data archival storage systems.
Cloud Computing
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present invention are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as Follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as Follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based email). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as Follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 10, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 includes
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 10 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 11, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 10) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 11 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 90 includes hardware and software
components. Examples of hardware components include: mainframes;
RISC (Reduced Instruction Set Computer) architecture based servers;
storage devices; networks and networking components. In some
embodiments, software components include network application server
software.
Virtualization layer 92 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers; virtual storage; virtual networks, including virtual
private networks; virtual applications and operating systems; and
virtual clients.
In one example, management layer 94 may provide the functions
described below. Resource provisioning provides dynamic procurement
of computing resources and other resources that are utilized to
perform tasks within the cloud computing environment. Metering and
Pricing provide cost tracking as resources are utilized within the
cloud computing environment, and billing or invoicing for
consumption of these resources. In one example, these resources may
include application software licenses. Security provides identity
verification for cloud consumers and tasks, as well as protection
for data and other resources. User portal provides access to the
cloud computing environment for consumers and system
administrators. Service level management provides cloud computing
resource allocation and management such that required service
levels are met. Service Level Agreement (SLA) planning and
fulfillment provide pre-arrangement for, and procurement of, cloud
computing resources for which a future requirement is anticipated
in accordance with an SLA.
Workloads layer 96 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation; software development and lifecycle
management; virtual classroom education delivery; data analytics
processing; transaction processing; and mobile desktop.
Computer Program Product
The present invention may be a system, a method, and/or a computer
program product. The computer program product may include a
computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
The descriptions of the various embodiments of the present
disclosure have been presented for purposes of illustration, but
are not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to explain the principles of the embodiments, the
practical application or technical improvement over technologies
found in the marketplace, or to enable others of ordinary skill in
the art to understand the embodiments disclosed herein.
"Present invention" does not create an absolute indication and/or
implication that the described subject matter is covered by the
initial set of claims, as filed, by any as-amended set of claims
drafted during prosecution, and/or by the final set of claims
allowed through patent prosecution and included in the issued
patent. The term "present invention" is used to assist in
indicating a portion or multiple portions of the disclosure that
might possibly include an advancement or multiple advancements over
the state of the art. This understanding of the term "present
invention" and the indications and/or implications thereof are
tentative and provisional and are subject to change during the
course of patent prosecution as relevant information is developed
and as the claims is amended.
"And/or" is the inclusive disjunction, also known as the logical
disjunction and commonly known as the "inclusive or." For example,
the phrase "A, B, and/or C," means that at least one of A or B or C
is true; and "A, B, and/or C" is only false if each of A and B and
C is false.
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